RAKE receivers are well known in the communication arts and find wide-spread use in Code Division Multiple Access (CDMA) systems, such as in IS-95, IS-2000 (cdma2000), Wideband CDMA (WCDMA), and High Speed Packet Access (HSPA) wireless communication networks. The name derives from the rake-like appearance of some representations of these receivers, wherein multiple, parallel receiver “fingers” corresponding to different signal delays are used to receive multiple signal images in a received multipath signal. By coherently combining the finger outputs in a RAKE combiner, the conventional RAKE receiver can use multipath reception to improve the Signal-to-Noise Ratio (SNR) of the received multipath signal.
However, as is known to those skilled in the art, the conventional RAKE receiver is optimal only in certain limited circumstances. For example, the presence of self-interference and multi-user access interference both degrade the performance of the conventional RAKE receiver. To that end, the assignee of the instant application has made application for several patents relating to the use of a “generalized” RAKE receiver (GRAKE) architecture, wherein receiver performance is improved by increasing the sophistication of combining weight generation.
In the GRAKE architecture, the combining weight calculations consider correlations of one or more signal impairments across RAKE fingers. For example, a GRAKE receiver may track noise correlations across those fingers. Generalized RAKE receivers also may include a comparatively larger number of fingers, such that extra fingers may be positioned off of the signal path delays. Indeed, a generalized RAKE receiver can gain performance improvements by shifting these extra fingers to optimize the signal-to-noise ratio (SNR) or signal-to-interference-plus-noise ratio (SINR) of the received signal. Correlations of signal impairments can also be used in SNR or SINR estimation. SINR estimation is used in power control as well as in monitoring link quality and rate adaptation.
By using its knowledge of how selected signal impairments are correlated across fingers, the GRAKE receiver can compensate the finger combining weights such that interference is suppressed and receiver performance is improved. Of course, the need to determine signal impairment correlations with sufficient accuracy and rapidity stands as a primary challenge associated with implementation of the GRAKE receiver. Example techniques for estimating impairment covariance in a GRAKE receiver are described in U.S. Pat. No. 7,848,387, titled “Receiver parametric covariance estimation for transmit diversity” and issued 7 Dec. 2010, the entire contents of which are incorporated herein by reference.
As can be seen in the aforementioned patent, practical symbol-level equalization and interference suppression techniques implemented in the GRAKE receiver utilize maximum-likelihood (ML) combining weights. These ML combining weights, wML, are based on statistical estimates of impairments to the received signal, as represented in an impairment covariance matrix Ru, and a channel estimate vector h, as follows:wML={circumflex over (R)}u−1ĥ.  (1)
Traditionally, one of the main challenges in improving GRAKE performance has been providing impairment covariance estimates with a sufficient quality. The mainstream approach to estimation has been to use pilot symbols with known contents. With this approach, the pilot symbols are de-spread, the known symbol values are subtracted from the de-spread pilot symbols, the remaining residuals are correlated, and the correlation results are averaged over the observation interval. Unfortunately, the estimation quality is limited by the number of available pilot symbols. For example, with the Common Pilot Channel (CPICH) available in the Universal Mobile Telecommunications System (UMTS), only ten symbols per 0.667-second slot are available. As a result, smoothing or filtering over multiple slots needs to be applied to obtain an acceptable variance of the noise estimation. Such smoothing is undesirable when the propagation channel or the interference environment changes from one transmission-time interval (TTI) to another.
One way around the problem of insufficient pilot symbols is to use MMSE weight construction:wMMSE={circumflex over (R)}d−1ĥ,  (2)where the data covariance Rd=Ru+hhH is estimated from data symbols with unknown contents, of which there are plenty. Unfortunately, due to the presence of the rank-1 term hhH, the data covariance Rd becomes poorly conditioned at high signal-to-interference-plus-noise ratios (SINRs), and the MMSE weights are significantly more sensitive to channel estimation errors at high SINR. In practice, MMSE weights do not provide acceptable performance at high SINR and at medium-to-high speeds, where channel estimate smoothing is limited.
Unused codes, e.g., free nodes in the Orthogonal Variable Spreading Factor (OVSF) code tree, and codes with known contents (e.g., certain control channels) have each been included in the covariance estimation symbol pool, in some estimation techniques. (If a code is known to be unused, then symbols de-spread from the received signal using include no data at all, which means that the de-spread symbols contain only the results of impairments. Thus, a known unused code can be regarded as providing the equivalent of known pilot symbols.) When around 120-150 symbols from such codes may be identified, the impairment covariance estimation quality starts to approach “genie” levels. In other words, the estimation quality approaches levels that result in interference suppression and/or equalization that is indistinguishable from that realized with perfect estimates of the impairment covariance.
In a UMTS system, between twenty and 60 symbols per slot can usually be obtained from the High-Speed Shared Control Channel (HS-SCCH) codes, which have a spreading factor of 128. If it were possible to detect just one unused High-Speed Physical Downlink Shared Channel, which has a spreading factor of 16, then the equivalent of 160 symbols would be available per slot, virtually guaranteeing a high-quality impairment covariance estimate. While the normal full-buffer scheduling strategy at a High-Speed Packet Access (HSPA) base station is to attempt to allocate as many High-Speed Physical Downlink Shared Channel (HS-PDSCH) codes as possible, i.e., to avoid leaving unused codes, there are numerous practical scenarios that do result in empty codes.
One approach to detecting unused HS-PDSCH codes is based on evaluating long-term signal power and interference power estimates. However, this approach requires long observation intervals for sufficient robustness, so it is viable only in scenarios where the code allocation patterns do not change, such as in test scenarios. In real network deployments, HS-PDSCH code allocations can change on a per-TTI basis. As a result, the determination of whether a code is used or not must be made at the same time scale.
To enable opportunistic utilization of unused codes to maximize equalization performance, it is therefore desirable to develop techniques for fast, TTI-based unused code detection, with a high-degree of decision robustness.